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Autism spectrum disorder (ASD) is one of the major developmental disordersaffecting children. Recently, it has been hypothesized that ASD is associatedwith atypical brain connectivities. A substantial body of researches usePearson's correlation coefficients, mutual information, or partial correlationto investigate the differences in brain connectivities between ASD and typicalcontrols from functional Magnetic Resonance Imaging (fMRI). However,correlation or partial correlation does not directly reveal causal influences -the information flow - between brain regions. Comparing to correlation,causality pinpoints the key connectivity characteristics and removes redundantfeatures for diagnosis.  In this paper, we propose a two-step method for large-scale and cyclic causaldiscovery from fMRI. It can identify brain causal structures without doinginterventional experiments. The learned causal structure, as well as the causalinfluence strength, provides us the path and effectiveness of information flow.With the recovered causal influence strength as candidate features, we thenperform ASD diagnosis by further doing feature selection and classification. Weapply our methods to three datasets from Autism Brain Imaging Data Exchange(ABIDE).  From experimental results, it shows that with causal connectivities, thediagnostic accuracy largely improves. A closer examination shows thatinformation flows starting from the superior front gyrus to default modenetwork and posterior areas are largely reduced. Moreover, all enhancedinformation flows are from posterior to anterior or in local areas. Overall, itshows that long-range influences have a larger proportion of reductions thanlocal ones, while local influences have a larger proportion of increases thanlong-range ones. By examining the graph properties of brain causal structure,the group of ASD shows reduced small-worldness.

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data